Do you create models in Python and want to leverage SAS Model Manager to govern, deploy, and/or monitor your models? If yes, you'll want to check out pzmm and the videos below.
pzmm (also known as Python Zip Model Management) is a sasctl module created and maintained on GitHub by SAS Model Manager R&D. The package enables users of SAS Model Manager on SAS Viya and SAS Open Model Manager to zip through the process of importing Python models into the common model repository. In order to facilitate model imports, the module allows completion of the following tasks:
fileMetadata.json
- specifies the file roles for the names of the input and output variables files, the Python score code file, and the Python pickle fileModelProperties.json
- used to set the model properties that are read during the import processinputVar.json
and outputVar.json
- used to set the input and output variables of the modeldmcas_fitstat.json
- optional file that provides the fit statistics that are associated with the imported model, which are either user-generated or data-generateddmcas_lift.json
and dmcas_roc.json
- optional files that provide the Lift and ROC plots associated with the imported model, which are data-generated*score.py
model file used for model scoringPlease check out this video where ScottLindauer walks through the use of pzmm with SAS Model Manager.
pzmm Explained
Here is a link to the notebook ScottLindauer used in this demo.
For more information about how SAS Model Manager supports models developed in Python and other languages, please check out the article: Organize and manage all types of analytic models and pipelines. For more information about registering Python models like scikit-learn, TensorFlow, and XGBoost using sasctl and PZMM module see these links below:
Now that you've successfully registered your Python model in SAS Model Manager, see how you can easily compare and contrast your model with all types of models directly within SAS Model Manager.
Quick SAS Model Manager Demo
Now that your Python model is deployed, see an example of using other consumable SAS Viya APIs. This demo shows how users can easily leverage automated modeling and automated decisioning, all managed by SAS Model Manager, using a custom application and SAS Viya APIs.
Consumable SAS Viya APIs Demo
To learn how to create and build your machine learning web application using SAS AutoML, check out this great SAS Users blog post by @paugre. For additional resources, please check out:
Interested to learn about other SAS Model Manager features? Please check out other "What's new with SAS Model Manager?" posts including:
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